Open Conceptual Data Models

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Open Conceptual Data Models

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Open Conceptual Data Models : 

© 2008 OpenLink Software, All rights reserved Open Conceptual Data Models Making the Conceptual Layer Real via HTTP based Linked Data (aka. Linked Data)

Situation Analysis : 

© 2008 OpenLink Software, All rights reserved Situation Analysis Linked Data Vision: The transition of the HTTP based Webs (Intranet, Extranet, or Internet) from a Webs of Linked Documents to Webs of interlinked Structured Data Items (aka: entities, data objects, resources) Concurrent trend in the IT industry: A recognition of the benefits of conceptual data models over logical data models The Big Question: To what extent does Linked Data support conceptual level data models ?

Open Conceptual Data Models : 

© 2008 OpenLink Software, All rights reserved Open Conceptual Data Models Topics: Conceptual & Logical Data Models Conceptual Models for the Semantic Web Realizing Conceptual Models through Ontologies & Linked Data Virtuoso’s RDF based Linked Data Views ADO.NET Data Services & the Entity Data Model

Data Model Layers : 

© 2008 OpenLink Software, All rights reserved Data Model Layers Physical How data is physically represented on disk Logical (aka logical schema) Expresses problem domain in terms of data management technology (tables / columns) e.g. relational schema Conceptual (aka conceptual schema) Purely semantic description of problem space Describes things (entities), their characteristics (attributes) & associations between things (relationships)

Logical Data Model : 

© 2008 OpenLink Software, All rights reserved Logical Data Model Most prominent of the three data model types Main focus of database driven applications Due to pervasiveness of relational database driven applications within the enterprise and across the Web Weaknesses Impedance mismatch Loss of semantics during development process Heterogeneous databases & interoperability

Logical Data Model Weaknesses : 

© 2008 OpenLink Software, All rights reserved Logical Data Model Weaknesses Impedance Mismatch SQL expresses queries in terms of tables / views => targets logical schema Normalization fragments the data model Entities & their attributes may be split across several tables Navigation between objects requires relational joins over two or more tables Table rows must be reconstituted into higher level conceptual entities Conceptual level data model is desirable to: Remove impedance mismatch Isolate application from changes to logical data model Provide framework for productive human level interaction

Logical Data Model Weaknesses : 

© 2008 OpenLink Software, All rights reserved Logical Data Model Weaknesses Loss of Semantic Fidelity During Development Process: Develop conceptual model (E-R modelling) Transform to logical model for implementation DBMS generates physical model Problems: Each move to a lower level model depreciates semantic fidelity of the higher level model Conceptual Model semantics fragmented across schema / business rules / application code Application & Users must understand logical data model Must be hardcoded or inferred (imperfectly) from system tables

Logical Data Model Weaknesses : 

© 2008 OpenLink Software, All rights reserved Logical Data Model Weaknesses Heterogeneous Databases & Interoperability Logical data model Describes problem domain in terms of tables/columns Requires costly table joins to navigate model Application Exposed to specifics of a particular vendor’s RDBMS In heterogeneous database environment, must handle Different SQL dialects Different schemas No explicit data model. No explicit semantics. Interoperability/integration = perpetual problem for IT depts

Conceptual Models for Linked Data Webs : 

© 2008 OpenLink Software, All rights reserved Conceptual Models for Linked Data Webs Explosion of User Generated Data from Web 2.0 applications and their Data Silos is driving the recognition of the need to move from logical to conceptual models, exemplified by: Microsoft’s Entity Data Model / Entity Framework W3C’s Semantic Web Project which includes powerful technologies for this paradigm shift such as: Resource Description Framework (RDF Data Model and Data Representation Formats) Web Ontology Language (OWL) SPARQL (Query Language, RESTful Interface, and Query Result Serialization Formats)

Benefits of Conceptual Models : 

© 2008 OpenLink Software, All rights reserved Benefits of Conceptual Models More faithfully represents human view of domain of interest Conceptual model & semantics Explicit & available globally Not implicit & fragmented across business logic / UI etc Better / explicit semantics facilitates move from “search” to “esoteric precision find” Much easier heterogeneous data integration User Generated Data is inherently heterogeneous & disparately located

Application Areas – Present & Future : 

© 2008 OpenLink Software, All rights reserved Application Areas – Present & Future Social Media, eCommerce, Distributed Collaborative Apps. Require shareable, standards-based, cross-platform conceptual views of data Data portability Needed as users maintain multiple points of presence & identity across – blogs, social network accounts etc. Open business models Require exchange & integration of large amounts of data Scientific research – sharing of knowledge & findings Requires transparent access to distributed heterogeneous data Requires database integration using global schema Autonomous intelligent agents Free humans from large-volume data processing

Semantic Web Project Technologies : 

© 2008 OpenLink Software, All rights reserved Semantic Web Project Technologies These technologies offer: Ontologies For representing common semantics Spanning databases, applications, enterprises, on-line communities Deliver shared conceptual model Provide common schemas (Dublin Core, FOAF, SIOC, GoodRelations etc) Common Semantics (Ontologies) & Common Data Representation (RDF) Enable cross data source querying using SPARQL Data across several databases (or data spaces) can be meshed, expanded, and explored Querying using proprietary APIs unnecessary Brute force data merging via code is unnecessary Open Data Formats, Platform Independence, Common Models Facilitate data portability, accessibility, and integration.

Realizing Conceptual Models : 

© 2008 OpenLink Software, All rights reserved Realizing Conceptual Models Ontologies Provide the building blocks for conceptual models Define the concepts and their relationships in a domain of interest (or world view) Describing Classes & Properties – Ontology Languages RDFS Introduces the notions of concepts (classes) & instances OWL Adds more vocabulary for describing: relations between classes cardinality richer typing of properties, etc.

Goodness of Fit : 

© 2008 OpenLink Software, All rights reserved Goodness of Fit RDF was designed from the ground up as a metadata data model RDF / RDFS / OWL work directly at the level of conceptual models Conceptual model terminology matches RDF/OWL terminology Concepts, entities, attributes, relationships. A natural fit! RDF lends itself naturally to describing conceptual models.

Semantic Expressivity Comparison : 

© 2008 OpenLink Software, All rights reserved Semantic Expressivity Comparison Data Definition Language (DDL)-based Relational Model Relationship between two entities isn’t explicit Foreign key relating two rows in separate tables doesn’t express the nature of the relationship Semantics must often be inferred from table definitions RDF-based Conceptual Model Relationship between two entities is stated explicitly by predicate in subject-predicate-object triple Semantic expressivity of RDF/RDFS/OWL is much better than DDL Has richer semantic content than equivalent DDL-based logical/relational model

RDF Conceptual Model – Artist / Records / Tracks : 

© 2008 OpenLink Software, All rights reserved RDF Conceptual Model – Artist / Records / Tracks

Global Granular Information Sharing : 

© 2008 OpenLink Software, All rights reserved Global Granular Information Sharing Traditional Logical/Relational Data Model Schema described by DDL is internal to DBMS Primary keys identifying an individual table row (i.e. entity instance) not globally unique, not easily usable outside host DBMS Gives rise to ‘data silos’ RDF’s use of Generic HTTP-based URIs Externalises the data and schema Makes both globally accessible & scalable Provides globally unique IDs for entities/relations/classes A vehicle for granular, global information sharing down to the equivalent of the record level.

Linked Data – What is It? : 

© 2008 OpenLink Software, All rights reserved Linked Data – What is It? A method for exposing, sharing & connecting data on HTTP based Data Networks. A term coined by Tim Berners-Lee that describes a RESTful mechanism for HTTP based Data Access & Manipulation by Reference A record level HTTP based Open Data Access & Connectivity mechanism A richer hyperlinking mechanism that takes us from Hypertext Links (Document to Document) to Hyperdata Links (Data Item to Data Item).

Linked Data – Why Is It Important : 

© 2008 OpenLink Software, All rights reserved Linked Data – Why Is It Important It exposes the compound nature of Data Containers (e.g., Documents) such that Data Containers are uniquely identified & referenceable Data Items within Data Containers are uniquely identified & referencable It provides a conceptual model oriented Open Data Access & Connectivity mechanism It delivers a powerful mechanism for meshing disparate and heterogeneous data sources.

Linked Data Model : 

© 2008 OpenLink Software, All rights reserved Linked Data Model Changes the focus from linked documents to linked entitiesThe document as a data container becomes less relevant

Hyperdata Links Between Data Items : 

© 2008 OpenLink Software, All rights reserved Hyperdata Links Between Data Items

Linked Data Benefits – Data Exploration : 

© 2008 OpenLink Software, All rights reserved Linked Data Benefits – Data Exploration Natural Navigation Through Typed Links RDF entities (instance data, classes, and properties) are identified by dereferencable HTTP URIs Navigating from one data item to another is easy via: Single LINK click from any HTTP user agent commences data item relationship navigation Linked Data Browers such as OpenLink Data Explorer Relational/Logical Model Cumbersome Requires SQL joins + typically Object-Relational mapping e.g. in C# : track = lennonAlbum.Tracks[“Imagine”]

Linked Data Benefits - Aggregatable Data : 

© 2008 OpenLink Software, All rights reserved Linked Data Benefits - Aggregatable Data Often desirable to have an integrated view of all the data available about an item or topic Database Realm Integration problematic, difficult to combine logical schemas Semantic Web Data aggregation is easy: every resource has a unique URI Individual items can be linked Conceptual models can be linked Cross-domain links enrich domain knowledge Different facets of the same data item may be described by different URIs minted by different authors Can be linked. e.g. owl:sameAs, rdf:type predicates May expose facts not directly represented in any one source

Linked Data – Data Aggregation : 

© 2008 OpenLink Software, All rights reserved Linked Data – Data Aggregation

Linked Data Benefits - Self Describing Data : 

© 2008 OpenLink Software, All rights reserved Linked Data Benefits - Self Describing Data Resource Description Framework (RDF) A technology for creating self-describing Web resources Data Item’s type definition ‘accompanies’ it via rdfs:type relations An RDF based data can be queried using SPARQL without knowing anything beforehand about the data definition (schema comes last in this realm) Provides the basis for powerful deductive data exploration tools Logical / Relational Schema Users / applications need a detailed understanding of the schema to use and navigate the data Application’s knowledge of the schema typically hardcoded Ad-hoc end-user data exploration potentially error prone

Linked Data Benefits - SPARQL : 

© 2008 OpenLink Software, All rights reserved Linked Data Benefits - SPARQL If a user agent has no built-in knowledge of a particular Data Item, it can dereference its Generic HTTP URI to obtain such information The Power of SPARQL Discover what sorts of things a data source contains select distinct ?URI ?ObjectType where { ?URI a ?ObjectType } Determine all the properties of an data item’s class select * where { <http://my.org/resourceTypes/Department> ?property ?hasValue } Determine all the properties and values of an data item instance DESCRIBE <http://my.org/resource/Accounts> No prior knowledge of the RDF data source is needed

Virtuoso - Linked Data Generation Options : 

© 2008 OpenLink Software, All rights reserved Virtuoso - Linked Data Generation Options Conceptual layer insulates Linked Data consumers from RDFization infrastructure & data source heterogeneity

Virtuoso RDF based Linked Data Views : 

© 2008 OpenLink Software, All rights reserved Virtuoso RDF based Linked Data Views Expose relational model data as RDF graph model data Provide the means to move from a logical model to a conceptual model view Available for querying through SPARQL or SPASQL (SPARQL embedded in SQL) No physical regeneration of relational data RDF Views = Virtuoso RDF Meta-Schema (MSL) + Meta-Schema Language MSL = A domain specific, declarative language for mapping a logical SQL data model to a conceptual RDF data model

Slide 29: 

© 2008 OpenLink Software, All rights reserved Northwind Demo Database:RDF View Definition Extract prefix northwind: <http://www.openlinksw.com/schemas/northwind#> … create iri class northwind:Customer <http://^{URIQADefaultHost}^/Northwind/Customer/%U#this> (in customer_id varchar not null) … alter quad storage virtrdf:DefaultQuadStorage … from Demo.demo.Customers as customers from Demo.demo.Orders as orders … { Demo.demo.Customers Northwind RDF View Definition create virtrdf:NorthwindDemo as graph iri (“http://^{URIQADefaultHost}^/Northwind”) { … northwind:Customer(customers.CustomerID) a foaf:Organization as virtrdf:Customer-CustomerID ; northwind:companyName customers.CompanyName as … ; … northwind:fax customers.Fax as virtrdf:Customer-fax . … } } northwind:Customer(orders.CustomerID)northwind:has_order northwind:Order(orders.OrderID) as virtrdf:Order-has_order .

Northwind Demo Database:Customer Table to RDF data item Mapping : 

© 2008 OpenLink Software, All rights reserved Northwind Demo Database:Customer Table to RDF data item Mapping Orders Table

LinqToRdf + Virtuoso : 

© 2008 OpenLink Software, All rights reserved LinqToRdf + Virtuoso

LinqToRdf to MusicBrainz - Conceptual Model Veneer : 

© 2008 OpenLink Software, All rights reserved LinqToRdf to MusicBrainz - Conceptual Model Veneer

ADO.NET Data Services & Entity Data Model : 

© 2008 OpenLink Software, All rights reserved ADO.NET Data Services & Entity Data Model A framework for exposing ‘pure data’ service over HTTP No support for RDF Fails to imbibe any of RDF’s inherent benefits Lack of platform independence & standards compliance Supports REST-style interfaces Supports Atom, JSON and XML payloads But Server-side: Windows only Consuming Astoria services at a higher level requires Windows .NET client or Silverlight-supported browser

ADO.NET Data Services & Entity Data Model : 

© 2008 OpenLink Software, All rights reserved ADO.NET Data Services & Entity Data Model Server-side only conceptual model Powerful URL addressing to query/navigate/sort/filter etc Customers collection:http://myserver/data.svc/Customers Customer ALFKI: http://myserver/data.svc/Customers('ALFKI') Customer ALFKI's orders: http://myserver/data.svc/Customers('ALFKI')/Orders But Client must know conceptual schema e.g. to construct above URIs Lack of Deferencable Entity IDs Ability to discover entities and dereference their descriptions (attributes/relations) is confined to the facilities offered by .NET c.f. SPARQL’s ability to handle unknown data sources

ADO.NET Data Services & Entity Data Model : 

© 2008 OpenLink Software, All rights reserved ADO.NET Data Services & Entity Data Model No Support for Non-SQL Data Sources Astoria is aimed exclusively at making relational data Web accessible c.f. Linked Data Realm Recognize that vast amounts of data resides in unstructured and semi-structured data sources Support for embedding RDF into existing (X)HTML RDFa, GRDDL, eRDF Emerging tools for converting non-RDF data to RDF model data Emerging tools for exposing Relational data as RDF Graph Model data Astoria lacks scalability & scope of Semantic Web technologies

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